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# The .dockerignore file excludes files from the container build process.
#
# https://docs.docker.com/engine/reference/builder/#dockerignore-file
# Exclude Git files
.git
.github
.gitignore
# Exclude Python cache files
__pycache__
.mypy_cache
.pytest_cache
.ruff_cache
# Exclude Python virtual environment
/venv
# Exclude output files
/outputs
output*.png
# Exclude models cache
/models
PhotoMaker/LICENSE 0 → 100755
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Tencent is pleased to support the open source community by making PhotoMaker available.
Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
PhotoMaker is licensed under the Apache License Version 2.0 except for the third-party components listed below.
Terms of the Apache License Version 2.0:
---------------------------------------------
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http://www.apache.org/licenses/
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PhotoMaker/MacGPUEnv.md 0 → 100644
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# How to use GPU on Mac M1/2
1. Install xcode tools
```
xcode-select --install
```
2. Install llvm
```
brew install llvm libomp
```
3. Install torch 2.1.2
```
pip install torch==2.1.2
```
\ No newline at end of file
PhotoMaker/README.md 0 → 100755
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<p align="center">
<img src="https://photo-maker.github.io/assets/logo.png" height=100>
</p>
<!-- ## <div align="center"><b>PhotoMaker</b></div> -->
<div align="center">
## PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding [![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-md-dark.svg)](https://huggingface.co/papers/2312.04461)
[[Paper](https://huggingface.co/papers/2312.04461)] &emsp; [[Project Page](https://photo-maker.github.io)] &emsp; [[Model Card](https://huggingface.co/TencentARC/PhotoMaker)] <br>
[[💥New 🤗 **Demo (PhotoMaker V2)**](https://huggingface.co/spaces/TencentARC/PhotoMaker-V2)] &emsp; [[🤗 Demo (Realistic)](https://huggingface.co/spaces/TencentARC/PhotoMaker)] &emsp; [[🤗 Demo (Stylization)](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style)] <br>
[[Replicate Demo (Realistic)](https://replicate.com/jd7h/photomaker)] &emsp; [[Replicate Demo (Stylization)](https://replicate.com/yorickvp/photomaker-style)] <be>
If the ID fidelity is not enough for you, please try our [PhotoMaker V2](https://huggingface.co/spaces/TencentARC/PhotoMaker-V2) or [stylization application](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style), you may be pleasantly surprised.
🥳 We release **PhotoMaker V2**. Please refer to [comparisons](./README_pmv2.md) between PhotoMaker V1, PhotoMaker V2, IP-Adapter-FaceID-plus-V2, and InstantID. Please watch [this video](https://photo-maker.github.io/assets/demo_pm_v2_full.mp4) for how to use our demo.
</div>
---
### 🌠 **Key Features:**
1. Rapid customization **within seconds**, with no additional LoRA training.
2. Ensures impressive ID fidelity, offering diversity, promising text controllability, and high-quality generation.
3. Can serve as an **Adapter** to collaborate with other Base Models alongside LoRA modules in community.
---
<a href="https://trendshift.io/repositories/7008" target="_blank" align=center><img src="https://trendshift.io/api/badge/repositories/7008" alt="TencentARC%2FPhotoMaker | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
❗❗ Note: If there are any PhotoMaker based resources and applications, please leave them in the [discussion](https://github.com/TencentARC/PhotoMaker/discussions/36) and we will list them in the [Related Resources](https://github.com/TencentARC/PhotoMaker?tab=readme-ov-file#related-resources) section in README file.
Now we know the implementation of **Replicate**, **Windows**, **ComfyUI**, and **WebUI**. Thank you all!
<div align="center">
![photomaker_demo_fast](https://github.com/TencentARC/PhotoMaker/assets/21050959/e72cbf4d-938f-417d-b308-55e76a4bc5c8)
</div>
## 🚩 **New Features/Updates**
- ✅ July 22, 2024. 💥 We release PhotoMaker V2 with **improved ID fidelity**. At the same time, it still maintains the generation quality, editability, and compatibility with any plugins that PhotoMaker V1 offers. We have also provided scripts for integration with [ControlNet](./inference_scripts/inference_pmv2_contronet.py), [T2I-Adapter](./inference_scripts/inference_pmv2_t2i_adapter.py), and [IP-Adapter](./inference_scripts/inference_pmv2_ip_adapter.py) to offer excellent control capabilities. Users can further customize scripts for upgrades, such as combining with LCM for acceleration or integrating with IP-Adapter-FaceID or InstantID to further improve ID fidelity. We will release technical report of PhotoMaker V2 soon. Please refer to [this doc](./README_pmv2.md) for a quick preview.
- ✅ January 20, 2024. An **important** note: For those GPUs that do not support bfloat16, please change [this line](https://github.com/TencentARC/PhotoMaker/blob/6ec44fc13909d64a65c635b9e3b6f238eb1de9fe/gradio_demo/app.py#L39) to `torch_dtype = torch.float16`, the speed will be **greatly improved** (1min/img (before) vs. 14s/img (after) on V100). The minimum GPU memory requirement for PhotoMaker is **11G** (Please refer to [this link](https://github.com/TencentARC/PhotoMaker/discussions/114) for saving GPU memory).
- ✅ January 15, 2024. We release PhotoMaker.
---
## 🔥 **Examples**
### Realistic generation
- [![Huggingface PhotoMaker](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/TencentARC/PhotoMaker)
- [**PhotoMaker notebook demo**](photomaker_demo.ipynb)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/BYBZNyfmN4jBKBxxt4uxz.jpeg" height=450>
</p>
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/9KYqoDxfbNVLzVKZzSzwo.jpeg" height=450>
</p>
### Stylization generation
Note: only change the base model and add the LoRA modules for better stylization
- [![Huggingface PhotoMaker-Style](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style)
- [**PhotoMaker-Style notebook demo**](photomaker_style_demo.ipynb)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/du884lcjpqqjnJIxpATM2.jpeg" height=450>
</p>
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/-AC7Hr5YL4yW1zXGe_Izl.jpeg" height=450>
</p>
# 🔧 Dependencies and Installation
- Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 2.0.0](https://pytorch.org/)
```bash
conda create --name photomaker python=3.10
conda activate photomaker
pip install -U pip
# Install requirements
pip install -r requirements.txt
# Install photomaker
pip install git+https://github.com/TencentARC/PhotoMaker.git
```
Then you can run the following command to use it
```python
from photomaker import PhotoMakerStableDiffusionXLPipeline
```
# ⏬ Download Models
The model will be automatically downloaded through the following two lines:
```python
from huggingface_hub import hf_hub_download
photomaker_path = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
```
You can also choose to download manually from this [url](https://huggingface.co/TencentARC/PhotoMaker).
# 💻 How to Test
## Use like [diffusers](https://github.com/huggingface/diffusers)
- Dependency
```py
import torch
import os
from diffusers.utils import load_image
from diffusers import EulerDiscreteScheduler
from photomaker import PhotoMakerStableDiffusionXLPipeline
### Load base model
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
base_model_path, # can change to any base model based on SDXL
torch_dtype=torch.bfloat16,
use_safetensors=True,
variant="fp16"
).to(device)
### Load PhotoMaker checkpoint
pipe.load_photomaker_adapter(
os.path.dirname(photomaker_path),
subfolder="",
weight_name=os.path.basename(photomaker_path),
trigger_word="img" # define the trigger word
)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
### Also can cooperate with other LoRA modules
# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="xl_more_art-full")
# pipe.set_adapters(["photomaker", "xl_more_art-full"], adapter_weights=[1.0, 0.5])
pipe.fuse_lora()
```
- Input ID Images
```py
### define the input ID images
input_folder_name = './examples/newton_man'
image_basename_list = os.listdir(input_folder_name)
image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
input_id_images = []
for image_path in image_path_list:
input_id_images.append(load_image(image_path))
```
<div align="center">
<a href="https://github.com/TencentARC/PhotoMaker/assets/21050959/01d53dfa-7528-4f09-a1a5-96b349ae7800" align="center"><img style="margin:0;padding:0;" src="https://github.com/TencentARC/PhotoMaker/assets/21050959/01d53dfa-7528-4f09-a1a5-96b349ae7800"/></a>
</div>
- Generation
```py
# Note that the trigger word `img` must follow the class word for personalization
prompt = "a half-body portrait of a man img wearing the sunglasses in Iron man suit, best quality"
negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth, grayscale"
generator = torch.Generator(device=device).manual_seed(42)
images = pipe(
prompt=prompt,
input_id_images=input_id_images,
negative_prompt=negative_prompt,
num_images_per_prompt=1,
num_inference_steps=num_steps,
start_merge_step=10,
generator=generator,
).images[0]
gen_images.save('out_photomaker.png')
```
<div align="center">
<a href="https://github.com/TencentARC/PhotoMaker/assets/21050959/703c00e1-5e50-4c19-899e-25ee682d2c06" align="center"><img width=400 style="margin:0;padding:0;" src="https://github.com/TencentARC/PhotoMaker/assets/21050959/703c00e1-5e50-4c19-899e-25ee682d2c06"/></a>
</div>
## Start a local gradio demo
Run the following command:
```python
python gradio_demo/app.py
```
You could customize this script in [this file](gradio_demo/app.py).
If you want to run it on MAC, you should follow [this Instruction](MacGPUEnv.md) and then run the app.py.
## Usage Tips:
- Upload more photos of the person to be customized to improve ID fidelity. If the input is Asian face(s), maybe consider adding 'Asian' before the class word, e.g., `Asian woman img`
- When stylizing, does the generated face look too realistic? Adjust the Style strength to 30-50, the larger the number, the less ID fidelity, but the stylization ability will be better. You could also try out other base models or LoRAs with good stylization effects.
- Reduce the number of generated images and sampling steps for faster speed. However, please keep in mind that reducing the sampling steps may compromise the ID fidelity.
# Related Resources
### Replicate demo of PhotoMaker:
1. [Demo link](https://replicate.com/jd7h/photomaker), run PhotoMaker on replicate, provided by [@yorickvP](https://github.com/yorickvP) and [@jd7h](https://github.com/jd7h).
2. [Demo link (style version)](https://replicate.com/yorickvp/photomaker-style).
### WebUI version of PhotoMaker:
1. **stable-diffusion-webui-forge**: https://github.com/lllyasviel/stable-diffusion-webui-forge provided by [@Lvmin Zhang](https://github.com/lllyasviel)
2. **Fooocus App**: [Fooocus-inswapper](https://github.com/machineminded/Fooocus-inswapper) provided by [@machineminded](https://github.com/machineminded)
### Windows version of PhotoMaker:
1. [bmaltais/PhotoMaker](https://github.com/bmaltais/PhotoMaker/tree/v1.0.1) by [@bmaltais](https://github.com/bmaltais), easy to deploy PhotoMaker on Windows. The description can be found in [this link](https://github.com/TencentARC/PhotoMaker/discussions/36#discussioncomment-8156199).
2. [sdbds/PhotoMaker-for-windows](https://github.com/sdbds/PhotoMaker-for-windows/tree/windows) by [@sdbds](https://github.com/sdbds).
### ComfyUI:
1. 🔥 **Official Implementation by [ComfyUI](https://github.com/comfyanonymous/ComfyUI)**: https://github.com/comfyanonymous/ComfyUI/commit/d1533d9c0f1dde192f738ef1b745b15f49f41e02
2. https://github.com/ZHO-ZHO-ZHO/ComfyUI-PhotoMaker
3. https://github.com/StartHua/Comfyui-Mine-PhotoMaker
4. https://github.com/shiimizu/ComfyUI-PhotoMaker
### Purely C/C++/CUDA version of PhotoMaker:
1. [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp/pull/179) by [@bssrdf](https://github.com/bssrdf).
### Other Applications / Web Demos
1. **Wisemodel 始智 (Easy to use in China)** https://wisemodel.cn/space/gradio/photomaker
2. **OpenXLab (Easy to use in China)**: https://openxlab.org.cn/apps/detail/camenduru/PhotoMaker
[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/camenduru/PhotoMaker)
by [@camenduru](https://github.com/camenduru).
3. **Colab**: https://github.com/camenduru/PhotoMaker-colab by [@camenduru](https://github.com/camenduru)
4. **Monster API**: https://monsterapi.ai/playground?model=photo-maker
5. **Pinokio**: https://pinokio.computer/item?uri=https://github.com/cocktailpeanutlabs/photomaker
### Graido demo in 45 lines
Provided by [@Gradio](https://twitter.com/Gradio/status/1747683500495691942)
# 🤗 Acknowledgements
- PhotoMaker is co-hosted by Tencent ARC Lab and Nankai University [MCG-NKU](https://mmcheng.net/cmm/).
- Inspired from many excellent demos and repos, including [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), [multimodalart/Ip-Adapter-FaceID](https://huggingface.co/spaces/multimodalart/Ip-Adapter-FaceID), [FastComposer](https://github.com/mit-han-lab/fastcomposer), and [T2I-Adapter](https://github.com/TencentARC/T2I-Adapter). Thanks for their great work!
- Thanks to the [HunyuanDiT](https://github.com/Tencent/HunyuanDiT) team for their generous support and suggestions!
- Thanks to the Venus team in Tencent PCG for their feedback and suggestions.
- Thanks to the HuggingFace team for their generous support!
# Disclaimer
This project strives to impact the domain of AI-driven image generation positively. Users are granted the freedom to create images using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.
# BibTeX
If you find PhotoMaker useful for your research and applications, please cite using this BibTeX:
```BibTeX
@inproceedings{li2023photomaker,
title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
PhotoMaker/README_pmv2.md 0 → 100755
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<p align="center">
<img src="https://photo-maker.github.io/assets/logo.png" height=70>
</p>
<!-- ## <div align="center"><b>PhotoMaker</b></div> -->
<div align="center">
## PhotoMaker V2: Improved ID Fidelity and Better Controllability Compared to PhotoMaker V1
[[🤗 Demo](https://huggingface.co/spaces/TencentARC/PhotoMaker-V2)]
</div>
When training PhotoMaker V2, we focused on improving ID fidelity. Compared to PhotoMaker V1, we introduced 1️⃣ new training strategies, incorporated 2️⃣ more portrait datasets, and utilized 3️⃣ a more powerful ID extraction encoder. We will release a technical report soon. Thank you all for your attention.
### 🌠 **Key improvements in PhotoMaker V2:**
1. **ID fidelity** has been **further improved**, especially for single image input and Asian facial inputs. Of course, feeding more facial images can still yield better results.
2. By integrating [ControlNet](./inference_scripts/inference_pmv2_contronet.py), [T2I-Adapter](./inference_scripts/inference_pmv2_t2i_adapter.py), and [IP-Adapter](./inference_scripts/inference_pmv2_ip_adapter.py), the generation process becomes **more controllable**. We provide corresponding scripts for reference. Additionally, PhotoMaker V2 allows users to achieve better ID consistency by combining it with IP-Adapter-FaceID, InstantID, and [character LoRA](https://github.com/TencentARC/PhotoMaker/discussions/14).
3. PhotoMaker V2 **inherits the promising features of PhotoMaker V1**, such as high-quality and diverse generation capabilities, and powerful text control. Additionally, it can still integrate previous applications like bringing characters from old photos or paintings back to reality, identity mixing, and changing age or gender.
## Comparisons with PhotoMaker V1, IP-Adapter-FaceID and InstantID
We selected the three most prevalent methods in ID personalization generation, namely PhotoMaker V1, [IP-Adapter-FaceID-Plus-V2](https://huggingface.co/h94/IP-Adapter-FaceID) ([best of IP-Adapter-FaceID](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/195)), and [InstantID](https://github.com/InstantID/InstantID).
To ensure a fair comparison, we used the same base model ([RealVisXL-V4.0](https://huggingface.co/SG161222/RealVisXL_V4.0)) and scheduler ([Euler](https://huggingface.co/docs/diffusers/api/schedulers/euler)), and selected the best out of four randomly generated images from each method for visualization. The prompts and negative prompts were consistent:
Prompt: `instagram photo, portrait photo of a woman img holding two cats, colorful, perfect face, natural skin, hard shadows, film grain`
Negative Prompt: `(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth`
We can see that our method has **advantages** in maintaining ID fidelity and in the quality of the generated images
![comp_pm_v2_reba](https://github.com/user-attachments/assets/b978ffa2-97c9-4910-ab23-a2b2edd3be1d)
![comp_pm_v2_musk](https://github.com/user-attachments/assets/6b96d65b-813a-45e0-8f7a-25041dc4dc10)
![comp_pm_v2_yanzu](https://github.com/user-attachments/assets/b788b2b0-9166-4c9d-aa46-24ef1fb4e5a9)
![comp_pm_v2_yifei](https://github.com/user-attachments/assets/66fa8a73-8973-4e40-a094-c4cb3eec8d8a)
## Cooperation with ControlNet / T2I-Adapter / IP-Adapter
PhotoMaker V2 can collaborate with [T2I-Adapter’s doodle mode](https://huggingface.co/TencentARC/t2i-adapter-sketch-sdxl-1.0),
allowing for controlled image generation based on user drawings and prompts.
This feature can be experienced in [[🤗 our official demo]](https://huggingface.co/spaces/TencentARC/PhotoMaker-V2).
The following video is an example of the experience process:
https://github.com/user-attachments/assets/1303d684-89e4-49d2-8e8c-4b659c8b48e7
Additionally, PhotoMaker V2 can work with [ControlNet](https://github.com/lllyasviel/ControlNet) and [T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) for layout control, such as edge, pose, depth, and more.
We provide two example scripts:
1. [inference_pmv2_contronet.py](./inference_scripts/inference_pmv2_contronet.py)
2. [inference_pmv2_t2i_adapter.py](./inference_scripts/inference_pmv2_t2i_adapter.py)
The image below is an example of controlled generation using pose through ControlNet:
![pm_v2_controlnet](https://github.com/user-attachments/assets/57767447-192c-4606-af2a-4206b5dbccf9)
Our sample scripts can be referred to:
[inference_pmv2_ip_adapter.py](./inference_scripts/inference_pmv2_ip_adapter.py)
The image below is an example:
![pm_v2_ipadapter](https://github.com/user-attachments/assets/89f95604-6cfa-4dde-b563-2d052bac14cc)
PhotoMaker V2, as a plugin, can work well with other plugins, such as IP-Adapter-FaceID or InstantID, to further improve ID fidelity, or combining with LCM for acceleration. We look forward to your exploration of more features, and welcome you to **provide PRs** or **contribute to the open-source community**
🥳 If you have built or known repositories or applications around PhotoMaker V2, please leave us a message in the discussion. We will include them in our README.
## LICENSE
Since PhotoMaker V2 relies on [InsightFace](https://github.com/deepinsight/insightface), it also needs to comply with its [license](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
PhotoMaker/cog.yaml 0 → 100644
View file @ 028e76f4
# Configuration for Cog ⚙️
# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
build:
# set to true if your model requires a GPU
gpu: true
# python version in the form '3.11' or '3.11.4'
python_version: "3.11"
python_packages:
- "accelerate==0.26.1"
- "diffusers==0.25.0"
- "huggingface-hub==0.20.2"
- "numpy==1.24.4"
- "omegaconf==2.3.0"
- "peft==0.7.1"
- "safetensors==0.4.1"
- "torch==2.1.1"
- "torchvision==0.16.1"
- "transformers==4.36.2"
run:
- curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.5.6/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
# predict.py defines how predictions are run on your model
predict: "predict.py:Predictor"
image: r8.im/tencentarc/photomaker
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